检索结果-内蒙古大学图书馆

Optimizing Chemical Phosphorus Removal for Wastewater Treatment: Insights from Binary Machine Learning Modeling with Elasticity and Productivity Evaluation

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Huang, Runyao Wang, Hongtao Mąkinia, Jacek Jin, Sitian Zhou, Zhen Zhang, Ming Yu, Chenyang Xie, Li College of Environmental Science and Engineering Key Laboratory of Yangtze River Water Environment Ministry of Education State Key Laboratory of Pollution Control and Resource Reuse Tongji University Shanghai200092 China UNEP-Tongji Institute of Environment for Sustainable Development Tongji University Shanghai200092 China Shanghai Institute of Pollution Control and Ecological Security Tongji University Shanghai200092 China Faculty of Civil and Environmental Engineering Gdańsk University of Technology Narutowicza Street 11/12 Gdańsk80-233 Poland Shanghai Engineering Research Center of Energy – Saving in Heat Exchange Systems College of Environmental and Chemical Engineering Shanghai University of Electric Power Shanghai200090 China Shanghai Chengtou Wastewater Treatment Co. Ltd Shanghai201203 China

Ensuring compliance with legal discharge regulations for total phosphorus (TP) is crucial in wastewater treatment plants (WWTPs), considering the minimization of chemical consumption like poly aluminum chloride (PAC). However, the relationship between actual TP removal and PAC consumption presents numerical complexities, hindering data-driven modeling efforts. This study analyzed daily observations over one-year period in WWTP to assess elasticity and productivity, revealing decoupling and congestion patterns between PAC consumption and TP removal. Binary dependent features were established for modeling. The results highlighted the importance of wastewater inflow and biological treatment features, suggesting PAC reduction to mitigate decoupling and congestion. Thus, insights were obtained for sustainable WWTP operations to sufficiently leverage influent characteristics and utilize biological treatment to minimize chemical dosing. The assessment workflow, incorporating elasticity and productivity considerations, establishes a robust basis for further optimization within a normalized production trajectory in wastewater sector, concurrently lowering resource depletion and enhancing environmental benefits. © 2024, The Authors. All rights reserved.

关键词： Wastewater treatment

Effective Throughput Optimization of SAG Milling Process Based on BPNN and Genetic Algorithm

学校读者我要写书评

暂无评论

Effective Throughput Optimization of SAG Milling Process Bas...

Industrial Cyber-Physical systems (ICPS)

作者： Zhenhong Liao Ce Xu Wen Chen Qifu Chen Feng Wang Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Mineral Resources Development and Utilization Changsha Research Institute of Mining and Metallurgy CO. LTD. Chagnsha China Hunan Valin Lianyuan Iron And Steel Co. Ltd. Loudi China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

Grinding is an energy-consuming process in mineral processing industry. Improving grinding processing capacity per unit power consumption is an effective means to reduce grinding production cost. In this paper, a new index for evaluating the effective processing throughput of SAG milling is proposed. The production process model is established by BP neural network (BPNN). Through combining the process mechanism and production constraints, the genetic algorithm is adopted to optimize the operating parameters of the SAG milling process to maximize the effective throughput, thus improving the grinding efficiency. The experimental results showed that through optimization of effective throughput function proposed in this paper, the SAG mill processing capacity has been increased by 4% and the operating power drawn reduced by 1.12%. It has important guiding significance for the actual production process.

关键词： Industries Power demand Neural networks Milling Throughput Minerals Materials processing

Overcoming the Fear of the Dark: Occlusion-Aware Model-Predictive Planning for Automated Vehicles Using Risk Fields

学校读者我要写书评

暂无评论

arXiv 2023年

作者： van der Ploeg, Chris Nyberg, Truls Sánchez, José Manuel Gaspar Silvas, Emilia van de Wouw, Nathan Netherlands Organisation for Applied Scientific Research Integrated Vehicle Safety Group Helmond5700 AT Netherlands Eindhoven University of Technology Dynamics and Control Group Mechanical Engineering Dept. P.O. Box 513 Eindhoven5600 MB Netherlands Eindhoven University of Technology Control Systems Technology Group Mechanical Engineering Dept. P.O. Box 513 Eindhoven5600 MB Netherlands KTH Royal Institute of Technology Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science Stockholm100 44 Sweden Scania CV AB Södertälje151 87 Sweden

As vehicle automation advances, motion planning algorithms face escalating challenges in achieving safe and efficient navigation. Existing Advanced Driver Assistance systems (ADAS) primarily focus on basic tasks, leaving unexpected scenarios for human intervention, which can be error-prone. Motion planning approaches for higher levels of automation in the state-of-the-art are primarily oriented toward the use of risk- or anti-collision constraints, using over-approximates of the shapes and sizes of other road users to prevent collisions. These methods however suffer from conservative behavior and the risk of infeasibility in high-risk initial conditions. In contrast, our work introduces a novel multi-objective trajectory generation approach. We propose an innovative method for constructing risk fields that accommodates diverse entity shapes and sizes, which allows us to also account for the presence of potentially occluded objects. This methodology is integrated into an occlusion-aware trajectory generator, enabling dynamic and safe maneuvering through intricate environments while anticipating (potentially hidden) road users and traveling along the infrastructure toward a specific goal. Through theoretical underpinnings and simulations, we validate the effectiveness of our approach. This paper bridges crucial gaps in motion planning for automated vehicles, offering a pathway toward safer and more adaptable autonomous navigation in complex urban contexts. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

Landscape of respiratory syncytial virus

学校读者我要写书评

暂无评论

Chinese Medical Journal 2024年第24期137卷 2953-2978页

作者： Yuping Duan Zimeng Liu Na Zang Bingbing Cong Yuqing Shi Lili Xu Mingyue Jiang Peixin Wang Jing Zou Han Zhang Ziheng Feng Luzhao Feng Lili Ren Enmei Liu You Li Yan Zhang Zhengde Xie School of Population Medicine and Public Health Chinese Academy of Medical Science and Peking Union Medical CollegeBeijing 100730China State Key Laboratory of Respiratory Health and Multimorbidity National Institute of Pathogen BiologyChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100029China National Health Commission Key Laboratory of Systems Biology of Pathogen Christophe Mérieux LaboratoryNational Institute of Pathogen BiologyChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 102629China Department of Respiratory Children’s Hospital of Chongqing Medical University National Clinical Research Center for Child Health and DisordersMinistry of Education Key Laboratory of Child Development and DisordersChina International Science and Technology Cooperation Base of Child Development and Critical DisordersChongqing 400014China Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity Key Laboratory of Children’s Important Organ Development and Diseases of Chongqing Municipal Health CommissionChongqing 400014China Department of Epidemiology National Vaccine Innovation PlatformSchool of Public HealthNanjing Medical UniversityNanjingJiangsu 211166China National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases NHC Key Laboratory of Medical Virology and Viral DiseaseNational Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 102206China Beijing Key Laboratory of Pediatric Respiratory Infection Diseases Key Laboratory of Major Diseases in ChildrenMinistry of EducationNational Clinical Research Center for Respiratory DiseasesNational Key Discipline of Pediatrics(Capital Medical University)Research Unit of Critical Infection in ChildrenChinese Academy of Medical Sciences(2019RU016)Beijing Pediatric Research InstituteBeijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijing 100045China Key Laborator

Respiratory syncytial virus(RSV)is an enveloped,negative-sense,single-stranded RNA virus of the Orthopneumovirus genus of the Pneumoviridae family in the order *** can cause acute upper and lower respiratory tract infections,sometimes with extrapulmonary *** disease burden of RSV infection is enormous,mainly affecting infants and older adults aged 75 years or ***,treatment options for RSV are largely *** strategies remain a critical focus,with efforts centered on vaccine development and the use of prophylactic monoclonal *** date,three RSV vaccines have been approved for active immunization among individuals aged 60 years and *** children who are not eligible for these vaccines,passive immunization is recommended.A newly approved prophylactic monoclonal antibody,Nirsevimab,which offers enhanced neutralizing activity and an extended half-life,provides exceptional protection for high-risk infants and young *** review provides a comprehensive and detailed exploration of RSV’s virology,immunology,pathogenesis,epidemiology,clinical manifestations,treatment options,and prevention strategies.

关键词： Respiratory syncytial virus Vaccine Lower respiratory tract infections Niservimab Epidemiology

Comparing heuristics, constraint optimization, and reinforcement learning for an industrial 2D packing problem

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Böhm, Stefan Neumayer, Martin Kramer, Oliver Schiendorfer, Alexander Knoll, Alois Research & Development Department Technische Hochschule Rosenheim Rosenheim Germany Chair of Robotics Artificial Intelligence and Real-time Systems Technische Universität München München Germany Institut für nachhaltige Energieversorgung Rosenheim Germany Research Institute AImotion Bavaria Technische Hochschule Ingolstadt Ingolstadt Germany

Cutting and Packing problems are occurring in different industries with a direct impact on the revenue of businesses. Generally, the goal in Cutting and Packing is to assign a set of smaller objects to a set of larger objects. To solve Cutting and Packing problems, practitioners can resort to heuristic and exact methodologies. Lately, machine learning is increasingly used for solving such problems. This paper considers a 2D packing problem from the furniture industry, where a set of wooden workpieces must be assigned to different modules of a trolley in the most space-saving way. We present an experimental setup to compare heuristics, constraint optimization, and deep reinforcement learning for the given problem. The used methodologies and their results get collated in terms of their solution quality and runtime. In the given use case a greedy heuristic produces optimal results and outperforms the other approaches in terms of runtime. Constraint optimization also produces optimal results but requires more time to perform. The deep reinforcement learning approach did not always produce optimal or even feasible solutions. While we assume this could be remedied with more training, considering the good results with the heuristic, deep reinforcement learning seems to be a bad fit for the given use case. © 2021, CC BY.

关键词： Constrained optimization

A Structured Prediction Approach for Robot Imitation Learning

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Duan, Anqing Batzianoulis, Iason Camoriano, Raffaello Rosasco, Lorenzo Pucci, Daniele Billard, Aude Robotics and Machine Intelligence Laboratory The Hong Kong Polytechnic University Hong Kong Learning Algorithms and Systems Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland Visual And Multimodal Applied Learning Laboratory Politecnico di Torino Turin Italy DIBRIS Università degli Studi di Genova Genoa Italy Laboratory for Computational and Statistical Learning IIT@MIT Istituto Italiano di Tecnologia Massachusetts Institute of Technology CambridgeMA United States Center Università di Genova Genoa Italy Dynamic Interaction Control research line Italian Institute of Technology Genoa Italy

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised learning that enables learning models to operate on output spaces with complex structures. Through the lens of structured prediction, we show how robots can learn to imitate trajectories belonging to not only Euclidean spaces but also Riemannian manifolds. Exploiting ideas from information theory, we propose a class of loss functions based on the f-divergence to measure the information loss between the demonstrated and reproduced probabilistic trajectories. Different types of f-divergence will result in different policies, which we call imitation modes. Furthermore, our approach enables the incorporation of spatial and temporal trajectory modulation, which is necessary for robots to be adaptive to the change in working conditions. We benchmark our algorithm against state-of-the-art methods in terms of trajectory reproduction and adaptation. The quantitative evaluation shows that our approach outperforms other algorithms regarding both accuracy and efficiency. We also report real-world experimental results on learning manifold trajectories in a polishing task with a KUKA LWR robot arm, illustrating the effectiveness of our algorithmic framework. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

Set-membership estimation in shared situational awareness for automated vehicles in occluded scenarios

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Narri, Vandana Alanwar, Amr Mårtensson, Jonas Norén, Christoffer Dal Col, Laura Johansson, Karl Henrik Research and Development Scania CV AB Södertälje151 87 Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE100 44 Sweden

One of the main challenges in developing autonomous transport systems based on connected and automated vehicles is the comprehension and understanding of the environment around each vehicle. In many situations, the understanding is limited to the information gathered by the sensors mounted on the ego-vehicle, and it might be severely affected by occlusion caused by other vehicles or fixed obstacles along the road. Situational awareness is the ability to perceive and comprehend a traffic situation and to predict the intent of vehicles and road users in the surrounding of the ego-vehicle. The main objective of this paper is to propose a framework for how to automatically increase the situational awareness for an automatic bus in a realistic scenario when a pedestrian behind a parked truck might decide to walk across the road. Depending on the ego-vehicle’s ability to fuse information from sensors in other vehicles or in the infrastructure, shared situational awareness is developed using a set-based estimation technique that provides robust guarantees for the location of the pedestrian. A two-level information fusion architecture is adopted, where sensor measurements are fused locally, and then the corresponding estimates are shared between vehicles and units in the infrastructure. Thanks to the provided safety guarantees, it is possible to appropriately adjust the ego-vehicle speed to maintain a proper safety margin. It is also argued that the framework is suitable for handling sensor failures and false detections in a systematic way. Three scenarios of growing information complexity are considered throughout the study. Simulations show how the increased situational awareness allows the ego-vehicle to maintain a reasonable speed without sacrificing safety. Copyright © 2021, The Authors. All rights reserved.

关键词： Roads and streets

Forecasting Slow-Wave Sleep Deficiency Through Stress-Related Markers in Forehead EEG

学校读者我要写书评

暂无评论

Forecasting Slow-Wave Sleep Deficiency Through Stress-Relate...

IEEE International Conference on systems, Man and Cybernetics

作者： Cheng-Hua Su Li-Wei Ko Tzyy-Ping Jung Julie Onton Shey-Cherng Tzou Jia-chi Juang Chung-Yao Hsu Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Science and Environmental Biology Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San Diego La Jolla CA USA College of Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute for Neural Computation University of California San Diego La Jolla CA USA Department of Neurology Sleep Disorders Center Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Neurology College of Medicine Kaohsiung Medical University Kaohsiung Taiwan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Sleep quality is critical for human well-being. Lack of sleep and poor sleep quality impair daily cognitive functions and health. While stress has been recognized as a detrimental factor on sleep quality, the relationship between pre-sleep stress level, resting EEG and subsequent sleep structure remains to be explored. This study presents a novel approach that evaluates pre-sleep stress levels using a 2-channel EEG to predict slow-wave sleep (SWS) deficiency. We recorded forehead EEG immediately before sleep onset, then utilized power spectra and entropy analysis to extract stress-related neurological features, including beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn), and spectral entropy (SpEn). We found that individuals with SWS deficiency exhibited signs of stress, such as a robust beta/delta correlation, higher alpha asymmetry, and increased FuzzEn. Conversely, individuals with ample SWS displayed weak beta/delta correlation and reduced FuzzEn in EEG recordings. Finally, we tested the robustness of the selected neuro markers with two supervised learning models and found that the selected markers predict SWS deficiency with an accuracy above 70%. Our study demonstrated that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. The proposed method can be integrated with a portable EEG device and sleep-improving interventions to develop a personalized sleep-improvement solution.

关键词： Correlation Forehead Sleep Supervised learning Feature extraction Brain modeling Electroencephalography Entropy Forecasting Stress

HD Map Generation from Vehicle Fleet Data for Highly Automated Driving on Highways

学校读者我要写书评

暂无评论

HD Map Generation from Vehicle Fleet Data for Highly Automat...

IEEE Symposium on Intelligent Vehicle

作者： Christopher Doer Michael Henzler Heiner Messner Gert F. Trommer Institute of Control Systems Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Mercedes Benz Research and Development North America (MBRDNA) Boulder CO USA

ISBN: (数字)9781728166735

ISBN: (纸本)9781728166742

In this paper we propose an iterative Graph SLAM based approach to create HD maps from series production vehicles fleet data. Only high level sensor measurements provided by advanced driver assistance systems are used. This reduces the required bandwidth and makes this approach scalable to vehicle fleet data. Creating HD maps from fleet data enables up to date HD maps since no dedicated mapping vehicles are required. At first, the data is aligned based on odometry, GNSS and traffic sign measurements. Next, road boundary measurements are included, which results in an optimized lateral alignment. Finally, lane boundary association can be carried out. This results in an HD map containing high accuracy data of traffic signs, road and lane boundaries. The approach was evaluated using series production vehicle fleet data recorded on US highways and on a German autobahn covering a distance of 35.4km. The final HD map was evaluated with a groundtruth HD map and achieved an average error of 0.59m.

关键词： Roads Simultaneous localization and mapping Global navigation satellite system Optimization Sparse matrices Pipelines Mathematical model